Endocrinology

Predicting metabolic syndrome risk in Chinese adults using integrated features of movement behaviors and dietary intake: a machine learning approach.

BACKGROUND: China is undergoing a rapid nutritional and epidemiological transition. Traditional epidemiological approaches often examine diet and physical activity in isolation, failing to capture the synergistic and non-linear effects of these lifestyle behaviors. This study aimed to develop an explainable machine learning (ML) framework to predict Metabolic Syndrome (MetS) risk using strictly no...

Volatilomics insights in older adults with diabetes: from metabolic mechanisms to precision geriatric medicine.

AIMS: This review aims to evaluate the hypothesis that Volatilomics-the comprehensive analysis of volatile organic compounds (VOCs) from breath, skin, urine, and other biological matrices-can serve as a non-invasive tool to characterize metabolic alterations associated with aging and diabetes mellitus in older adults, supporting precision geriatric medicine. METHODS: We conducted a narrative revie...

Role of Gut Microbiota in Psychiatric Disorders: From Mechanistic Insights to Therapeutic Strategies.

Mental health disorders are a global health challenge, and the underlying biological mechanisms remain unclear. Recent evidence has linked gut microbi...

A predictive model for significant periodontal disease progression: A large-scale cohort study.

BACKGROUND: The progression of periodontitis is challenging to predict. This study aimed to develop and validate a machine learning model to identify ...

Real-World Prospective Validation and Economic Evaluation of Deep Learning- Based Diabetic Retinopathy Detection From Fundus Photographs: A Systematic Review and Meta-analysis.

BACKGROUND: Deep learning (DL) has shown promise in delivering diagnostic and economic benefits for detecting diabetic retinopathy (DR) from fundus ph...

Adverse reactions of PD-1/PD-L1 inhibitors in cancer: FAERS database analysis and protocols to mitigate immune-related events in elderly patients and when using pembrolizumab and atezolizumab.

OBJECTIVE: This study aimed to characterize adverse drug reactions (ADRs) associated with programmed death-1/programmed death-ligand 1 (PD-1/PD-L1) in...

Integrated approaches to preventing macrovascular complications in type 2 diabetes mellitus: Advances in secondary prevention.

BACKGROUND: Type 2 diabetes mellitus (T2DM) substantially increases the risk of macrovascular complications, including coronary artery disease, cerebr...

Retrieval Augmented Generation (RAG) for Evaluating Regulatory Compliance of Drug Information and Clinical Trial Protocols.

The purpose was to evaluate retrieval-augmented generative (RAG) artificial intelligence (AI) methods for assessing the regulatory compliance of drug ...

Diabetes Spotlight: Martin Myers, MD, PhD-Understanding Metabolism and Glucose Through Neural Networks.

Feasibility of an Augmented Reality Artificial Intelligence Tool for Real-Time Assessment of Thyroid Fine-Needle Aspiration Adequacy.

PURPOSE: To evaluate the feasibility of developing an artificial intelligence application for real-time specimen adequacy assessment during ultrasound...

Future-aware blood glucose forecasting using knowledge distillation with transformer-based sequence-to-sequence models.

Accurate blood glucose level (BGL) forecasting is critical for diabetes self-management and clinical decision-making. Although deep learning models ba...

Gut Microbiota and Metabolic Health: From Dysbiosis to Therapeutics.

The gut microbiota (GM) is a pivotal regulator of host metabolism and a contributor to the pathophysiology of obesity, type 2 diabetes (T2D), and meta...

Evaluating Accuracy of ChatGPT-4o in Automated Carbohydrate Estimation From Images as a Self-Management Tool for Adolescents With Type 1 Diabetes.

INTRODUCTION: Type 1 diabetes mellitus (T1D) requires precise carbohydrate estimation to manage blood glucose and prevent chronic and acute complicati...

Predicting non-alcoholic fatty liver disease (NAFLD) using machine learning algorithms: Evidence from a large-scale community cohort in Taiwan.

Closely associated with metabolic disorders, non-alcoholic fatty liver disease (NAFLD) substantially increases the risk of hepatocellular carcinoma. T...

Automatic acromegaly detection using deep learning on hand images: a multicenter observational study.

CONTEXT: Acromegaly poses clinical challenges in terms of early diagnosis and intervention. Therefore, the development of novel diagnostic tools is es...

Artificial intelligence to investigate metabolomics data for precision medicine.

BACKGROUND: Metabolomic data offers insights into disease mechanisms, diagnostics, and therapeutic targets by analyzing metabolic profiles. In analyzi...

Multi-dimensional dynamic monitoring of gut microbiota-emotion axis and precision exercise intervention in compulsory drug rehabilitation: A 24-week randomized controlled trial.

BACKGROUND/OBJECTIVES: Substance use disorders (SUDs) present a global health challenge with high relapse rates. Emerging evidence implicates gut micr...

Effectiveness of natural language intelligence technology in chronic diseases nursing: A systematic review and meta-analysis.

BACKGROUND: Chronic diseases pose a heavy global burden, with challenges in utilizing unstructured data for continuous care. Natural language intellig...

Deep Learning Model to Detect Diabetic Retinopathy in 45° Images Using Ground Truth from Ultra-Widefield Imaging.

PURPOSE: To evaluate the performance of a deep learning (DL) model in classifying diabetic retinopathy (DR) severity using fundus images with varying ...

Machine learning-enabled early risk stratification of β-Iactam-induced electrolyte imbalances.

AIMS: To estimate the incidence of β-lactam/β-lactamase inhibitor - associated electrolyte imbalances and develop an internally validated, interpretab...